The present invention relates generally to neuromorphic processing devices, and more particularly to such devices employing assemblages of neuron circuits based on resistive memory cells.
Neuromorphic technology relates to computing systems which are inspired by biological architectures of the nervous system. The conventional computing paradigm based on CMOS logic and von Neumann architecture is becoming increasingly inadequate to meet the expanding processing demands placed on modern computer systems. Compared to biological systems, it is also highly inefficient in terms of power consumption and space requirements. These issues have prompted a significant research effort to understand the highly efficient computational paradigm of the human brain and to create artificial cognitive systems with unprecedented computing power.
Neurons, along with synapses, are basic computational units in the brain. A neuron can integrate the input signals it receives. In biological neurons, a thin lipid-bilayer membrane is used to separate the electrical charge inside of the cell from that outside of it. The membrane potential, which represents the stored neuron state, is progressively modified by the arrival of neuron input signals. When the membrane potential traverses a specific voltage threshold, the neuron will “fire”, generating an output signal known as an “action potential” or “spike”, and then revert to its initial state. These spikes are conveyed to other neurons via synapses which change their connection strength (“plasticity” or “synaptic weight”) as a result of neuronal activity.
Most current artificial neuron realizations are based on hybrid analog/digital VLSI circuits and require several transistors to be realized. Emulating the integrate-and-fire neuronal functionality with conventional CMOS circuits, such as current-mode, voltage-mode and subthreshold transistor circuits, is relatively complex and hinders seamless integration with highly-dense synaptic arrays. Moreover, conventional CMOS solutions rely on storing the membrane potential in a capacitor. Even with a drastic scaling of the technology node, realizing the capacitance densities measured in biological neuronal membranes (approximately 10 fF/μm2) is challenging.
Resistive memory cells such as phase change memory (PCM) cells have been recognized as suitable candidates for the realization of neural hardware (see e.g. “The Ovonic Cognitive Computer—A New Paradigm”, Ovshinsky, Proc. E/PCOS, 2004, and “Novel Applications Possibilities for Phase-Change Materials and Devices”, Wright et al., Proc. E/PCOS, 2013). Resistive memory cells are programmable-resistance devices which rely on the variable resistance characteristics of a volume of resistive material disposed between a pair of electrodes. These cells are memristors, i.e., devices that remember the history of the current that has flowed through them. While scalable and efficient memristive synapses have been demonstrated, concrete realizations for practical artificial neurons that can capture most of the essential attributes of biological neurons are more challenging.
In addition to the deterministic neuronal dynamics, stochastic neuronal dynamics play a key role in signal encoding and transmission in biological systems. IEEE JESTCS, Vol. 5, No. 2, June 2015, “Memristors Empower Spiking Neurons With Stochasticity”, Al-Shedivat et al., discloses use of a metal-oxide memristor to artificially inject intra-neuron stochasticity in a circuit neuron implementation.